Synergistic miRNA Research Articles

Cardiac tissue-resident vesicles differentially modulate anti-fibrotic phenotype by age and sex through synergistic miRNA effects

Aging is a risk factor for cardiovascular disease, the leading cause of death worldwide. Cardiac fibrosis is a harmful result of repeated myocardial infarction that increases risk of morbidity and future injury. Interestingly, both rates and outcomes of cardiac fibrosis differ between young and aged individuals, as well as men and women. Here, for the first time, we identify and isolate matrix-bound extracellular vesicles from the left ventricles (LVs) of young or aged males and females in both human and murine models. These LV vesicles (LVVs) show differences in morphology and content between these four cohorts in both humans and mice. LVV effects on fibrosis were also investigated in vitro, and aged male LVVs were pro-fibrotic while other LVVs were anti-fibrotic. From these LVVs, we could identify therapeutic miRNAs to promote anti-fibrotic effects. Four miRNAs were identified and together, but not individually, demonstrated significant cardioprotective effects when transfected. This suggests that miRNA synergy can regulate cell response, not just individual miRNAs, and also indicates that biological agent-associated therapeutic effects may be recapitulated using non-immunologically active agents. Furthermore, that chronic changes in LVV miRNA content may be a major factor in sex- and age-dependent differences in clinical outcomes of cardiac fibrosis.

Prediction and Boolean logical modelling of synergistic microRNA regulatory networks during reprogramming of male germline pluripotent stem cells

Unipotent male germline stem (GS) cells can undergo spontaneous reprogramming to germline pluripotent stem (GPS) cells during in vitro culture. In our previous study, we proposed a Boolean logical model of gene regulatory network (GRN) during reprogramming of GS cells to GPS cells. This study was designed to predict and model synergistic microRNA (miRNA) regulatory network during reprogramming of GS cells into GPS cells. The miRNAs targeting differentially expressed genes (DEGs) among GS and GPS cells were predicted by a novel Gene Ontology (GO) enrichment analysis to construct miRNA synergistic networks (MSN) and identify regulatory miRNA modules. Qualitative Boolean logical model of synergistic miRNAs and its regulated genes was then constructed by considering discrete, asynchronous, multivalued logical formalism using the GINsim modeling and simulation tools. Topology, functional and community overlap studies revealed that mmu-miR-200b-3p, mmu-miR-429–3p and mmu-miR-141–3p, mmu-miR-200a-3p and mmu-miR-200c-3p in MSN belongs to the family of miR-200/429/141 and conjectured to control the pluripotency and reprogramming by promoting Mesenchymal to Epithelial Transition (MET). Synergistic network involving mmu-miR-20b-5p, mmu-miR-20a-5p, mmu-miR-106a-5p, mmu-miR-106b-5p, and mmu-miR-17–5p were found to be essential for the maintenance of GS cells. Logical miRNA regulatory network modelling showed that synergistic miRNAs regulates the gene dynamics of MET during GS-GPS reprogramming, as confirmed by perturbation analysis. Taken together, our study predicted novel synergistic miRNAs involved in the regulation of reprogramming and pluripotency in GPS cells. The Boolean logical model of synergistic miRNAs regulatory network further confirms our previous study that gene dynamics of MET regulates GS-GPS reprogramming.

MiRCoop: Identifying Cooperating miRNAs via Kernel Based Interaction Tests.

Although miRNAs can cause widespread changes in expression programs, single miRNAs typically induce mild repression on their targets. Cooperativity among miRNAs is reported as one strategy to overcome this constraint. Expanding the catalog of synergistic miRNAs is critical for understanding gene regulation and for developing miRNA-based therapeutics. In this study, we develop miRCoop to identify synergistic miRNA pairs that have weak or no repression on the target mRNA individually, but when act together, induce strong repression. miRCoop uses kernel-based statistical interaction tests, together with miRNA and mRNA target information. We apply our approach to patient data of two different cancer types. In kidney cancer, we identify 66 putative triplets. For 64 of these triplets, there is at least one common transcription factor that potentially regulates all participating RNAs of the triplet, supporting a functional association among them. Furthermore, we find that identified triplets are enriched for certain biological processes that are relevant to kidney cancer. Some of the synergistic miRNAs are very closely encoded in the genome, hinting a functional association among them. In applying the method on tumor data with the primary liver site, we find 3105 potential triplet interactions. We believe miRCoop can aid our understanding of the complex regulatory interactions in different health and disease states of the cell and can help in designing miRNA-based therapies. Matlab code for the methodology is provided in https://github.com/guldenolgun/miRCoop.

MiR-124 and miR-203 synergistically inactivate EMT pathway via coregulation of ZEB2 in clear cell renal cell carcinoma (ccRCC)

BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most aggressive urological malignancies. MicroRNAs (miRNAs) are post-transcriptional gene regulators in tumor pathophysiology. As miRNAs exert cooperative repressive effects on target genes, studying the miRNA synergism is important to elucidate the regulation mechanism of miRNAs.MethodsWe first created a miRNA-mRNA association network based on sequence complementarity and co-expression patterns of miRNA-targets. The synergism between miRNAs was then defined based on their expressional coherence and the concordance between target genes. The miRNA and mRNA expression were detected in RCC cell lines (786-O) using quantitative RT-PCR. Potential miRNA-target interaction was identified by Dual-Luciferase Reporter assay. Cell proliferation and migration were assessed by CCK-8 and transwell assay.ResultsA synergistic miRNA–miRNA interaction network of 28 miRNAs (52 miRNA pairs) with high coexpression level were constructed, among which miR-124 and miR-203 were identified as most tightly connected. ZEB2 expression is inversely correlated with miR-124 and miR-203 and verified as direct miRNA target. Cotransfection of miR-124 and miR-203 into 786-O cell lines effectively attenuated ZEB2 level and normalized renal cancer cell proliferation and migration. The inhibitory effects were abolished by ZEB2 knockdown. Furthermore, pathway analysis suggested that miR-124 and miR-203 participated in activation of epithelial-to-mesenchymal transition (EMT) pathway via regulation of ZEB2.ConclusionsOur findings provided insights into the role of miRNA–miRNA collaboration as well as a novel therapeutic approach in ccRCC.

MicroRNAs tend to synergistically control expression of genes encoding extensively-expressed proteins in humans.

Considering complicated microRNA (miRNA) biogenesis and action mechanisms, it was thought so high energy-consuming for a cell to afford simultaneous over-expression of many miRNAs. Thus it prompts that an alternative miRNA regulation pattern on protein-encoding genes must exist, which has characteristics of energy-saving and precise protein output. In this study, expression tendency of proteins encoded by miRNAs’ target genes was evaluated in human organ scale, followed by quantitative assessment of miRNA synergism. Expression tendency analysis suggests that universally expressed proteins (UEPs) tend to physically interact in clusters and participate in fundamental biological activities whereas disorderly expressed proteins (DEPs) are inclined to relatively independently execute organ-specific functions. Consistent with this, miRNAs that mainly target UEP-encoding mRNAs, such as miR-21, tend to collaboratively or even synergistically act with other miRNAs in fine-tuning protein output. Synergistic gene regulation may maximize miRNAs’ efficiency with less dependence on miRNAs’ abundance and overcome the deficiency that targeting plenty of genes by single miRNA makes miRNA-mediated regulation high-throughput but insufficient due to target gene dilution effect. Furthermore, our in vitro experiment verified that merely 25 nM transfection of miR-21 be sufficient to influence the overall state of various human cells. Thus miR-21 was identified as a hub in synergistic miRNA–miRNA interaction network. Our findings suggest that synergistic miRNA–miRNA interaction is an important endogenous miRNA regulation mode, which ensures adequate potency of miRNAs at low abundance, especially those implicated in fundamental biological regulation.

CancerNet: a database for decoding multilevel molecular interactions across diverse cancer types

Protein–protein interactions (PPIs) and microRNA (miRNA)–target interactions are important for deciphering the mechanisms of tumorigenesis. However, current PPI databases do not support cancer-specific analysis. Also, no available databases can be used to retrieve cancer-associated miRNA–target interactions. As the pathogenesis of human cancers is affected by several miRNAs rather than a single miRNA, it is needed to uncover miRNA synergism in a systems level. Here for each cancer type, we constructed a miRNA–miRNA functionally synergistic network based on the functions of miRNA targets and their topological features in that cancer PPI network. And for the first time, we report the cancer-specific database CancerNet (http://bis.zju.edu.cn/CancerNet), which contains information about PPIs, miRNA–target interactions and functionally synergistic miRNA–miRNA pairs across 33 human cancer types. In addition, PPI information across 33 main normal tissues and cell types are included. Flexible query methods are allowed to retrieve cancer molecular interactions. Network viewer can be used to visualize interactions that users are interested in. Enrichment analysis tool was designed to detect significantly overrepresented Gene Ontology categories of miRNA targets. Thus, CancerNet serves as a comprehensive platform for assessing the roles of proteins and miRNAs, as well as their interactions across human cancers.

Bioinformatic analysis of microRNA expression in Parkinson's disease.

Parkinson's disease (PD) is a type of movement disorder caused by loss of dopamine‑producing neurons in the midbrain. In order to identify the synergistic microRNA (miRNA) pattern in PD, miRNA and mRNA double expression profiles of PD were downloaded. Differentially expressed miRNA and mRNA were identified [P<0.01, following false discovery rate (FDR) correction]. A cumulative hypergeometric distribution test was then performed to identify synergistic miRNAs (P<0.01, following FDR correction). Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations were performed to analyze the miRNA regulatory target genes. Subsequently, a synergistic miRNA network was constructed and miRNAs exhibiting a high degree were identified. In total, 200 differentially expressed miRNA and 2,966 differentially expressed mRNA were identified. In addition, 1,502 synergistic miRNA interactions were identified, and miRNAs regulated 304 target genes in total. The GO and KEGG analysis demonstrated that these target genes were enriched in biosynthetic and cellular biosynthetic processes, the assembly of cellular components in morphogenesis, mitogen‑activated protein kinase signaling, myometrial relaxation and contraction pathways as well as calcium regulation. The miRNA network demonstrated that miR‑627, miR‑634, miR‑514, miR‑563 and miR‑613 had a high degree. miRNA with a high degree may be associated with the pathogenesis of PD and, therefore, may assist in the diagnosis and therapy of PD.

Mirsynergy: detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion.

Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules. We propose Mirsynergy, an efficient deterministic overlapping clustering algorithm adapted from a recently developed framework. Mirsynergy operates in two stages: it first forms MiRMs based on co-occurring microRNA (miRNA) targets and then expands each MiRM by greedily including (excluding) mRNAs into (from) the MiRM to maximize the synergy score, which is a function of miRNA-mRNA and gene-gene interactions. Using expression data for ovarian, breast and thyroid cancer from The Cancer Genome Atlas, we compared Mirsynergy with internal controls and existing methods. Mirsynergy-MiRMs exhibit significantly higher functional enrichment and more coherent miRNA-mRNA expression anti-correlation. Based on Kaplan-Meier survival analysis, we proposed several prognostically promising MiRMs and envisioned their utility in cancer research. Mirsynergy is implemented/available as an R/Bioconductor package at www.cs.utoronto.ca/∼yueli/Mirsynergy.html.

MicroRNAs Synergistically Regulate Milk Fat Synthesis in Mammary Gland Epithelial Cells of Dairy Goats

Synergistic regulation among microRNAs (miRNAs) is important to understand the mechanisms underlying the complex molecular regulatory networks in goats. Goat milk fat synthesis is driven by a gene network that involves many biological processes in the mammary gland. These biological processes are affected by several miRNAs rather than a single miRNA. Therefore, identifying synergistic miRNAs is necessary to further understand the functions of miRNAs and the metabolism of goat milk fat synthesis. Using qRT-PCR, we assessed the expression of 11 miRNAs that have the potential to regulate milk fat synthesis in the goat mammary gland. Six of these miRNAs exhibited expression during the lactation cycle. Additionally, we also found that prolactin, the key hormone that regulates lactation, promotes the expression of four miRNAs (miR-23a, miR-27b, miR-103, and miR-200a). Further functional analysis showed that overexpression of all four miRNAs by using recombinant adenovirus in goat mammary gland epithelial cells can affect gene mRNA expression associated with milk fat synthesis. Specifically, elevated miR-200a expression suppressed the mRNA expression of genes involved in fat droplet formation. To analyze the synergistic regulation among these four miRNAs (miR-23a, miR-27b, miR-103, and miR-200a), we used the Pearson correlation coefficient to evaluate the correlation between their expression levels in 30 lactating goats. As a result, we found a strong correlation and mutual regulation between three miRNA pairs (miR-23a and miR-27b, miR-103 and miR-200a, miR-27b and miR-200a). This study provides the first experimental evidence that miRNA expression is synergistically regulated in the goat mammary gland and has identified the potential biological role of miRNAs in goat milk fat synthesis. The identification of synergistic miRNAs is a crucial step for further understanding the molecular network of milk fat synthesis at a system-wide level.

Dissection of Protein Interactomics Highlights MicroRNA Synergy

Despite a large amount of microRNAs (miRNAs) have been validated to play crucial roles in human biology and disease, there is little systematic insight into the nature and scale of the potential synergistic interactions executed by miRNAs themselves. Here we established an integrated parameter synergy score to determine miRNA synergy, by combining the two mechanisms for miRNA-miRNA interactions, miRNA-mediated gene co-regulation and functional association between target gene products, into one single parameter. Receiver operating characteristic (ROC) analysis indicated that synergy score accurately identified the gene ontology-defined miRNA synergy (AUC = 0.9415, p<0.001). Only a very small portion of the random miRNA-miRNA combinations generated potent synergy, implying poor expectancy of widespread synergy. However, targeting more key genes made two miRNAs more likely to act synergistically. Compared to other miRNAs, miR-21 was a highly exceptional case due to frequent appearance in the top synergistic miRNA pairs. This result highlighted its essential role in coordinating or strengthening physiological and pathological functions of other miRNAs. The synergistic effect of miR-21 and miR-1 were functionally validated for their significant influences on myocardial apoptosis, cardiac hypertrophy and fibrosis. The novel approach established in this study enables easy and effective identification of condition-restricted potent miRNA synergy simply by concentrating the available protein interactomics and miRNA-target interaction data into a single parameter synergy score. Our results may be important for understanding synergistic gene regulation by miRNAs and may have significant implications for miRNA combination therapy of cardiovascular disease.

Dissection of the potential characteristic of miRNA–miRNA functional synergistic regulations

MicroRNAs (miRNAs), as master gene regulators, control various cellular processes through the post-transcriptional regulatory network. miRNA synergetic regulations are gradually identified by experimental or computational evidence. However, the potential characteristic underlying miRNA synergism remains a mystery. Based on the constructed miRNA-miRNA functional synergetic networks (MFSNs), we attempted to systematically dissect their potential characteristic from three levels: sequence, secondary structure and transcriptional regulation. As a result, functional synergetic miRNA pairs exhibit high seed sequence and secondary structure similarity, and these characteristics are significant even when analyzing synergism between miRNAs from different families. Additionally, synergistic miRNAs tend to share a common transcriptional regulatory mechanism. A striking property of miRNAs with both synergism and co-regulation is their central roles in the MFSN, and tends to be involved in complex diseases. The potential characteristic in these three levels are validated to be robust by analyzing different MFSNs. Finally, we identified four tightly cooperative miRNA sub-networks which are also co-regulated by transcription factors, all of which regulate many important functions, including embryonic development, oncogenesis and micromanagement of metastasis, helping us to further understand the "language" of transcriptional and post-transcriptional regulation from a cross-layer view.

Discovery of microRNA–mRNA modules via population-based probabilistic learning

MicroRNAs (miRNAs) and mRNAs constitute an important part of gene regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e. computationally predicted target genes of miRNAs and their respective expression profiles. We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our method's ability for finding coherent miRNA-mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways.